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Found 7 papers, 6 papers with code
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially.
Practical Defences Against Model Inversion Attacks for Split Neural Networks
We describe a threat model under which a split network-based federated learning system is susceptible to a model inversion attack by a malicious computational server.
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
We introduce PyVertical, a framework supporting vertical federated learning using split neural networks.
Privacy and Trust Redefined in Federated Machine Learning
A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures.
Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C).
A Distributed Trust Framework for Privacy-Preserving Machine Learning
Privacy-preserving techniques distribute computation in order to ensure that data remains in the control of the owner while learning takes place.
Phishing URL Detection Through Top-level Domain Analysis: A Descriptive Approach
Phishing is considered to be one of the most prevalent cyber-attacks because of its immense flexibility and alarmingly high success rate.
